Many robots are programmed to utilize one or more end effectors to grasp one or more objects. For example, a robot may utilize a grasping end effector such as an “impactive” grasping end effector or “ingressive” grasping end effector (e.g., physically penetrating an object using pins, needles, etc.) to pick up an object from a first location, move the object to a second location, and drop off the object at the second location. Some additional examples of robot end effectors that may grasp objects include “astrictive” grasping end effectors (e.g., using suction or vacuum to pick up an object) and one or more “contigutive” grasping end effectors (e.g., using surface tension, freezing or adhesive to pick up an object), to name just a few.
While humans innately know how to correctly grasp many different objects, determining an appropriate manner to grasp an object for manipulation of that object may be a difficult task for robots. Despite the difficulty, approaches have been proposed in which robots fully-autonomously grasp various objects. However, some fully-autonomous approaches may suffer from one or more drawbacks, such as failure to autonomously generate grasp candidates for some objects and/or failure of some attempted grasps that are autonomously generated.
Moreover, in view of the difficulty of the grasping task for robots, techniques have been proposed in which a “human-in-the-loop” may utilize a graphical user interface to fully specify a full pose (position and orientation) of an end effector for a grasp of an object by a robot. For instance, in one approach a full 3D representation of the end effector and a full 3D representation of the object may be presented to the user via the graphical user interface. Using the graphical user interface, the user may manipulate the 3D representation of the end effector relative to the 3D representation of the object to specify the full pose of the end effector for the grasp of the object. However, some human-in-the-loop approaches may suffer from one or more drawbacks. For example, some may be relatively demanding of a human's time in fully specifying a full pose and/or relatively demanding of computational resources in generating a manipulable full 3D representation of an end effector and object. Also, for example, some may require that full 3D representations of the end effector and of the object be available. Additional and/or alternative drawbacks of these and/or other approaches may be presented.